Forecasting power requirements for an electrical grid is a complex task. A forecast that underestimates demand may result in a brown-out or a blackout. A forecast that overestimates demand may result in generation of unused power at considerable expense.
System operators may rely on short-term (e.g., next-day to ten days ahead) hourly load forecasting to set the day-ahead generation schedule for meeting tomorrow's loads. Known techniques for short-term forecasting focuses on the use of neural networks to produce day-ahead load forecasts. The premise for such techniques is that neural networks are well suited to the problem of modeling the nonlinear relationship between system loads and weather.
Unfortunately, known technology does not provide precise near-term (e.g., five-minute ahead to one hour-ahead) real-time load forecasts, such as at the five-minute level of load resolution. During this near-term range, system operators make critical operating decisions, including selection of generation units to be dispatched to meet system demand. While statistical modeling may forecast the short-term demand, a system operator may manually craft a near-term load forecast.
Next-day and near-term load forecasts are often developed by different groups within the organization. As the day unfolds, system operators make adjustments to the generation schedule that was set during the previous day to account for projected near-term system imbalances. In most cases, these real-time adjustments do not feed forward to help set the next-day forecast. Thus, many system operators are left with a mixed bag of forecasting techniques and no means of producing a single operational forecast that runs from five minutes ahead to several days ahead.